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Compromising Pareto-Optimality With Regularity in Platform-Based Multiobjective Optimization
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AI
DOI：10.1109/TEVC.2023.3336715.png)
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Abstract
En 中文
Multiobjective optimization problems give rise to a set of Pareto-optimal (PO) solutions, each of which makes a tradeoff among the objectives. When multiple PO solutions are to be implemented for different applications as platform-based solutions, a solution principle common to them is highly desired for easier understanding, implementation, and management purposes. In this article, we propose a systematic search methodology that deviates from finding PO solutions, but finds a set of near PO solutions sharing common principles of a desired structure and still possessing a tradeoff among objectives. After proposing the regular evolutionary multiobjective optimization (RegEMO) algorithm, we first demonstrate its working principle on a number of constrained and unconstrained multiobjective test problems. Thereafter, we demonstrate the practical significance of the proposed approach to a number of engineering design problems. Searching for a set of solutions with common principles of desire, rather than theoretical PO solutions without any common structure, is a practically meaningful task and this article should encourage more such practice-oriented developments of evolutionary multiobjective optimization in the near future.
Keywords:
Optimization
Task analysis
Systematics
Statistics
Sociology
Shape
Knowledge engineering
Evolutionary
multiobjective optimization
Pareto-optimal (PO) solutions
platform-based designs
regularity
Journal
IF:
12
Papers:
1.9K
Citations:
2.4W
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Cited Papers
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